Bottom Line:
In order to make evaluation, we compared the segmentation results of the proposed system with empirical contours.Independent features significance test indicates that our feature combination is significant for overlapping nuclei.Experimental results showed that our unsupervised approach with proposed feature combination yields acceptable performance for detection of overlapping nuclei.

Background: The extraction of overlapping cell nuclei is a critical issue in automated diagnosis systems. Due to the similarities between overlapping and malignant nuclei, misclassification of the overlapped regions can affect the automated systems' final decision. In this paper, we present a method for detecting overlapping cell nuclei in Pap smear samples.

Method: Judgement about the presence of overlapping nuclei is performed in three steps using an unsupervised clustering approach: candidate nuclei regions are located and refined with morphological operations; key features are extracted; and candidate nuclei regions are clustered into two groups, overlapping or non-overlapping, A new combination of features containing two local minima-based and three shape-dependent features are extracted for determination of the presence or absence of overlapping. F1 score, precision, and recall values are used to evaluate the method's classification performance.

Results: In order to make evaluation, we compared the segmentation results of the proposed system with empirical contours. Experimental results indicate that applied morphological operations can locate most of the nuclei and produces accurate boundaries. Independent features significance test indicates that our feature combination is significant for overlapping nuclei. Comparisons of the classification results of a fuzzy clustering algorithm and a non-fuzzy clustering algorithm show that the fuzzy approach would be a more convenient mechanism for classification of overlapping.

Conclusion: The main contribution of this study is the development of a decision mechanism for identifying overlapping nuclei to further improve the extraction process with respect to the segmentation of interregional borders, nuclei area, and radius. Experimental results showed that our unsupervised approach with proposed feature combination yields acceptable performance for detection of overlapping nuclei.

Fig7: Variation of local minima points. Twotextural features are observed on twenty cells for two groups as overlappedand non-overlapped. a) Difference ofdistances in cases of overlapping is shown. b) Variation of number of local minima inside the region isshown.

Mentions:
In this equation, the Nc term is the number of the common-valued pixels in twoimages. Na is the number of pixel values which occur only in image a, and Nb is thenumber of pixel values which occur only in image b. We extracted three binary imagesfrom each sample image for comparison. The first holds regions segmented bycomputer, and the other two are the empirical areas, segmented by two differentexpert observers. A comparison of the observers’ segmentations were accepted asground truth. The segmentation success for the 290 nuclei is given inTable 2.In this study, we propose a newcombination of features containing two local minima-based features in addition toshape-dependent features. The two textural features extracted for 20 previouslysegmented nuclei are shown in Figure 7, tohighlight the variance of such features in the presence of overlapping.Table 2

Fig7: Variation of local minima points. Twotextural features are observed on twenty cells for two groups as overlappedand non-overlapped. a) Difference ofdistances in cases of overlapping is shown. b) Variation of number of local minima inside the region isshown.

Mentions:
In this equation, the Nc term is the number of the common-valued pixels in twoimages. Na is the number of pixel values which occur only in image a, and Nb is thenumber of pixel values which occur only in image b. We extracted three binary imagesfrom each sample image for comparison. The first holds regions segmented bycomputer, and the other two are the empirical areas, segmented by two differentexpert observers. A comparison of the observers’ segmentations were accepted asground truth. The segmentation success for the 290 nuclei is given inTable 2.In this study, we propose a newcombination of features containing two local minima-based features in addition toshape-dependent features. The two textural features extracted for 20 previouslysegmented nuclei are shown in Figure 7, tohighlight the variance of such features in the presence of overlapping.Table 2

Bottom Line:
In order to make evaluation, we compared the segmentation results of the proposed system with empirical contours.Independent features significance test indicates that our feature combination is significant for overlapping nuclei.Experimental results showed that our unsupervised approach with proposed feature combination yields acceptable performance for detection of overlapping nuclei.

Background: The extraction of overlapping cell nuclei is a critical issue in automated diagnosis systems. Due to the similarities between overlapping and malignant nuclei, misclassification of the overlapped regions can affect the automated systems' final decision. In this paper, we present a method for detecting overlapping cell nuclei in Pap smear samples.

Method: Judgement about the presence of overlapping nuclei is performed in three steps using an unsupervised clustering approach: candidate nuclei regions are located and refined with morphological operations; key features are extracted; and candidate nuclei regions are clustered into two groups, overlapping or non-overlapping, A new combination of features containing two local minima-based and three shape-dependent features are extracted for determination of the presence or absence of overlapping. F1 score, precision, and recall values are used to evaluate the method's classification performance.

Results: In order to make evaluation, we compared the segmentation results of the proposed system with empirical contours. Experimental results indicate that applied morphological operations can locate most of the nuclei and produces accurate boundaries. Independent features significance test indicates that our feature combination is significant for overlapping nuclei. Comparisons of the classification results of a fuzzy clustering algorithm and a non-fuzzy clustering algorithm show that the fuzzy approach would be a more convenient mechanism for classification of overlapping.

Conclusion: The main contribution of this study is the development of a decision mechanism for identifying overlapping nuclei to further improve the extraction process with respect to the segmentation of interregional borders, nuclei area, and radius. Experimental results showed that our unsupervised approach with proposed feature combination yields acceptable performance for detection of overlapping nuclei.